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I n t r o d u c t i o n t o Decision Support Systems

I n t r o d u c t i o n t o Decision Support Systems. Decision Support, e-Business, and OLAP. Decision Support, E-Business, and OLAP. Professor Jason Chen School of Business Gonzaga University Spokane, WA 99258 chen@jepson.gonzaga.edu. mbus633. Objectives.

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I n t r o d u c t i o n t o Decision Support Systems

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  1. I n t r o d u c t i o n t o Decision Support Systems Decision Support, e-Business, and OLAP Decision Support, E-Business, and OLAP Professor Jason Chen School of Business Gonzaga University Spokane, WA 99258 chen@jepson.gonzaga.edu mbus633

  2. Objectives • Identify the changes taking place in the form and use of decision support in E-Business enterprises. • Identify the role and reporting alternatives of management information systems. • Describe how online analytical processing can meet key information needs of managers. • Explain the decision support system concept and how it differs from traditional management information systems.

  3. Objectives (cont.) • Explain how executive information systems can support the information needs of executives and managers. • Explain organizations are warehousing and mining data. • Give examples of several ways expert systems can be used in business decision-making situations.

  4. Internet Extranet Intranet Enterprise Information Portal Gateway Enterprise Information Portal User Interface Search Agents OLAP Data Mining DSS Knowledge Management What-If Models Sensitivity Models Goal-Seeking Models Optimization Models Database Management Functions Data Mart Operational Database Other Business Applications Analytical Database Knowledge Base Enterprise Information Portals and DSS

  5. Decisions in the E-Business Decision Characteristics Planning and Control of Overall Organizational Direction by Top Management Unstructured Strategic Management Planning and Control of Organizational Subunits by Middle Management Semi-structured Information Decisions Tactical Management Structured Planning and Control of Day to Day Operations by Supervisory Management Operational Management

  6. SALES & MANUFACTURING FINANCE ACCOUNTING HUMAN MARKETING RESOURCES Types of the Information Systems KIND OF SYSTEM GROUPS SERVED STRATEGIC LEVEL SENIOR (ESS,EIS,DSS) MANAGERS MANAGEMENT LEVEL MIDDLE (DSS, MIS) MANAGERS KNOWLEDGE LEVEL KNOWLEDGE & (KWS) DATA WORKERS OPERATIONAL OPERATIONAL LEVEL (TPS,OAS) MANAGERS

  7. Major Management Information Systems (DBMS) Reports Periodic Scheduled Reports Exception Reports Demand Reports and Responses Push Reports Management Information System (DBMS) Reports

  8. DBMS MBMS DGMS The Decision Support Systems Software System Task Environment DBMS: DataBase Management Systems MBMS: ModelBase Management Systems DGMS: DialoGue Management Systems User

  9. Important Decision Support Systems Analytical Models What If-Analysis Sensitivity Analysis Goal-Seeking Analysis Optimization Analysis Decision Support Systems

  10. Data is retrieved from corporate databases and staged in an OLAP multi-dimensional database Corporate Databases OLAP Server Client PC Multi- dimensional database • Operational DB • Data Marts • Data Warehouse Web-enabled OLAP Software OnLine Analytical Processing (OLAP)

  11. Tools used in the User Interface A variety of tools used by OLAP to query and analyze data stored in data warehouse and data marts: • Traditional query and reporting tools (SQL, QBE, QBF) • Spreadsheets • Data Mining tools. • Data Visualization tools. *

  12. COMPONENTS OF DATA WAREHOUSE

  13. Three-layer data warehouse architecture Quality, Integrity, and Historical Data 2. EDW 1. Operational data and systems 3. DM

  14. Definitions • Data Warehouse: An integrated and consistent store of subject-oriented data that is obtained from a variety of sources and formatted into a meaningful context to support decision-making in an organization. • Bill Inmon, the acknowledged father of the Data Warehouse, defines it as an integrated, subject-oriented, time-variant, non-volatile database that provides support for decision making.

  15. OLAP Activities • Generating queries • Requesting ad hoc reports • Conducting statistical and other analyses • Developing multimedia applications

  16. Using SQL for Querying • SQL (Structured Query Language)Data language English-like, nonprocedural, very user friendly language,Free formatExample:SELECT Name, SalaryFROM EmployeesWHERE Salary >2000

  17. Data Mining • Knowledge discovery in databases • Knowledge extraction • Data archeology • Data exploration • Data pattern processing • Data dredging • Information harvesting

  18. Data Mining Examples • A telephone company used a data mining tool to analyze their customer’s data warehouse. The data mining tool found about 10,000 supposedly residential customers that were expending over $1,000 monthly in phone bills. • After further study, the phone company discovered that they were really small business owners trying to avoid paying business rates *

  19. Other Data Mining Examples • 65% of customers who did not use the credit card in the last six months are 88% likely to cancel their accounts. • If age < 30 and income <= $25,000 and credit rating < 3 and credit amount > $25,000 then the minimum loan term is 10 years. • 82% of customers who bought a new TV 27" or larger are 90% likely to buy an entertainment center within the next 4 weeks.

  20. $75 Example of drill-down (a) Summary report (b) Drill-down with color added TM 14-21

  21. Multidimensionality • 3-D + Spreadsheets (OLAP has this) • Data can be organized the way managers like to see them, rather than the way that the system analysts do • Different presentations of the same data can be arranged easily and quickly • Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry • Measures: money, sales volume, head count, inventory profit, actual versus forecast • Time: daily, weekly, monthly, quarterly, or yearly

  22. Slicing a data cube

  23. Regions Salespersons Slicing a data cube

  24. Multidimensionality Limitations • Extra storage requirements • Higher cost • Extra system resource and time consumption • More complex interfaces and maintenanceMultidimensionality is especially popular in executive information and support systems (EIS and ESS)

  25. Data Visualization and Multidimensionality Data Visualization Technologies • Digital images • Geographic information systems • Graphical user interfaces • Multidimensions • Tables and graphs • Virtual reality • Presentations • Animation

  26. Geographic Information Systems (GIS) • A computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps • Spatially-oriented databases • Useful in marketing, sales, voting estimation, planned product distribution • Available via the Web • Can use with GPS (Global Positioning System)

  27. Business Intelligence on the Web • Can capture and analyze data from Web • Tools deployed on Web

  28. OLTP OLAP (On Line Transaction Processing On Line Analytical Processing) Current data Short database transactions Online update/insert/delete Normalization is promoted High volume transactions Transaction recovery is necessary Low number of concurrent users Various ad hoc queries • Current and historical data • Long database transaction • Batch update/insert/delete • De-normalization is promoted • Low volume transactions • Transaction recovery is not necessary • Low number of concurrent users • More predefined queries, but are efficient in processing numerous ad hoc queries. Requires numerous indexing (approx. 50% data)

  29. Internet Extranet Intranet Enterprise Information Portal Gateway Enterprise Information Portal User Interface Search Agents OLAP Data Mining DSS Knowledge Management What-If Models Sensitivity Models Goal-Seeking Models Optimization Models Database Management Functions Data Mart Operational Database Other Business Applications Analytical Database Knowledge Base Enterprise Information Portals and DSS

  30. Artificial Intelligence Cognitive Science Applications Robotics Applications Natural Interface Applications • Expert Systems • Fuzzy Logic • Genetic Algorithms • Neural Networks • Visual Perceptions • Locomotion • Navigation • Tactility • Natural Language • Speech Recognition • Multisensory Interface • Virtual Reality Artificial Intelligence Applications

  31. Interface Tutors Search Agents Presentation Agents User Interface Agents Information Management Agents Information Brokers Network Navigation Agents Information Filters Role- Playing Agents Intelligent Agents N

  32. The Expert System Expert Advice Knowledge Base User Interface Programs Inference Engine Program Workstation User Expert System Development Knowledge Engineering Knowledge Acquisition Program Expert and/or Knowledge Engineer Workstation Components of Expert Systems

  33. Major Application Categories of Expert Systems Decision Management Diagnostic/Troubleshooting Maintenance/Scheduling Design/Configuration Selection/Classification Process Monitoring/Control Expert System Applications

  34. eBusiness Key Concepts • eBusiness • The strategy of how to automate old business models with the aid of technology to maximize customer value • eCommerce • The process of buying and selling over digital media • eCRM (eCustomer Relationship Management) • The process of building, sustaining, and improving eBusiness relationships with existing and potential customers through digital media

  35. Focus on e-Business Applications Knowledge Management/Business Intelligence E-Commerce E-Customer Relationship Procurement Network Trading Network E-Channel Management Businesses & Consumers Businesses M:1 M:N 1:N E-Portal Management E-Services SCM/ERP/Legacy Appls

  36. The E-Business Application Architecture

  37. Data Mining Application Areas • Marketing • Banking • Retailing and sales • Manufacturing and production • Brokerage and securities trading • Insurance • Computer hardware and software • Government and defense • Airlines • Health care • Broadcasting • Law enforcement

  38. Intelligent Data Mining • Use intelligent search to discover information within data warehouses that queries and reports cannot effectively reveal • Find patterns in the data and infer rules from them • Use patterns and rules to guide decision making and forecasting • Five common types of information that can be yielded by data mining: 1) association, 2) sequences, 3) classifications, 4) clusters, and 5) forecasting

  39. Main Tools Used in Intelligent Data Mining • Case-based Reasoning • Neural Computing • Intelligent Agents • Other Tools • Decision trees • Rule induction • Data visualization

  40. Major Data Mining Characteristics and Objectives • Data are often buried deep • Client/server architecture • Sophisticated new tools--including advanced visualization tools--help to remove the information “ore” • End-user miner empowered by data drills and other power query tools with little or no programming skills • Often involves finding unexpected results • Tools are easily combined with spreadsheets, etc. • Parallel processing for data mining

  41. How does a Company to Survive and/or Prosper? • To survive and/or prosper in the turbulent e-Age, an organization should focus on three areas: • Core competencies, • Business models, and • Execution • Operations • People • Strategies

  42. Summary • Decision support systems in business are changing. The growth of corporate intranets, extranets, and other web technologies have increased the demand for a variety of personalized, proactive, web-enabled analytical techniques to support DSS. • Information systems must support a variety of management decision-making levels and decisions. These include the three levels of management activity: strategic, tactical, and operational.

  43. Summary (cont’d) • Online analytical processing (OLAP) is used to analyze complex relationships among large amounts of data stored in multidimensional databases. Data mining analyzes large stores of historical data contained in data warehouses. • Decision support systems are interactive computer-based information systems that use DSS software and a model base to provide information to support semi-structured and unstructured decision making.

  44. Summary (cont’d) • The major application domains in artificial intelligence include a variety of applications in cognitive sciences, robotics, and natural interfaces. • Organizations are warehousing and mining data.

  45. Homework • Complete an OLAP assignment on #2 of Chapter 6 (p.211) of the text • Copy the file CardiologyCategorical.xls • What you should turn in • Send an email containing the file with the work done • A hardcopy with answers for questions #2 (i.e., a thru e) [use of Word is required]

  46. Break !

  47. Virtual Reality • An environment and/or technology that provides artificially generated sensory cues sufficient to engender in the user some willing suspension of disbelief • Can share data and interact • Can analyze data by creating a landscape • Useful in marketing, prototyping aircraft designs • VR over the Internet through VRML

  48. Neural Networks AI Application Areas in Business Fuzzy Logic Systems Genetic Algorithms Virtual Reality Intelligent Agents Expert Systems AI Application Areas in Business

  49. Data Extract Data Cleanup Data Load Update Process Management Platform 6 7 Information Delivery System Metadata ODS Data Mining Tools 1 MRDB Transform Load OLAP Tools Data Warehouse DBMS MDDB 3 8 Data Marts Report Query EIS Tools 4 Admin Platform Legacy & External Data Applications & Tools Repository 2 5 Data Warehouse and Operational Data Stores.

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